DWTFormer: a frequency-spatial features fusion model for tomato leaf disease identification

Plant Methods, Journal Year: 2025, Volume and Issue: 21(1)

Published: March 11, 2025

Remarkable inter-class similarity and intra-class variability of tomato leaf diseases seriously affect the accuracy identification models. A novel disease model, DWTFormer, based on frequency-spatial feature fusion, was proposed to address this issue. Firstly, a Bneck-DSM module designed extract shallow features, laying groundwork for deep extraction. Then, dual-branch mapping network (DFMM) multi-scale features from frequency spatial domain information. In branch, 2D discrete wavelet transform decomposition effectively captured rich information in image, compensating convolution PVT (Pyramid Vision Transformer)-based developed global local enabling comprehensive representation. Finally, dual-domain fusion model dynamic cross-attention fuse features. Experimental results dataset demonstrated that DWTFormer achieved 99.28% accuracy, outperforming most existing mainstream Furthermore, 96.18% 99.89% accuracies have been obtained AI Challenger 2018 PlantVillage datasets. In-field experiments an 97.22% average inference time 0.028 seconds real plant environments. This work has reduced impact identification. It provides scalable reference fast accurate

Language: Английский

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TrioConvTomatoNet-BiLSTM: An Efficient Framework for the Classification of Tomato Leaf Diseases in Real Time Complex Background Images

International Journal of Computational Intelligence Systems, Journal Year: 2025, Volume and Issue: 18(1)

Published: April 10, 2025

Language: Английский

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Enhancing crop disease recognition via prompt learning-based progressive Mixup and Contrastive Language-Image Pre-training dynamic calibration

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 152, P. 110805 - 110805

Published: April 14, 2025

Language: Английский

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Identify Subtle Fall Hazards Using Transfer Learning

Published: April 22, 2025

Language: Английский

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Smart Plant Disease Diagnosis Using Multiple Deep Learning and Web Application Integration

Journal of Agriculture and Food Research, Journal Year: 2025, Volume and Issue: 21, P. 101948 - 101948

Published: April 23, 2025

Language: Английский

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TomaFDNet: A multiscale focused diffusion-based model for tomato disease detection

Frontiers in Plant Science, Journal Year: 2025, Volume and Issue: 16

Published: April 24, 2025

Tomatoes are one of the most economically significant crops worldwide, with their yield and quality heavily impacted by foliar diseases. Effective detection these diseases is essential for enhancing agricultural productivity mitigating economic losses. Current tomato leaf disease methods, however, encounter challenges in extracting multi-scale features, identifying small targets, complex background interference. The model Tomato Focus-Diffusion Network (TomaFDNet) was proposed to solve above problems. utilizes a focus-diffusion network (MSFDNet) alongside an efficient parallel convolutional module (EPMSC) significantly enhance extraction features. This combination particularly strengthens model's capability detect targets amidst backgrounds. Experimental results show that TomaFDNet reaches mean average precision (mAP) 83.1% detecting Early_blight, Late_blight, Leaf_Mold on leaves, outperforming classical object algorithms, including Faster R-CNN (mAP = 68.2%) You Only Look Once (YOLO) series (v5: mAP 75.5%, v7: 78.3%, v8: 78.9%, v9: 79%, v10: 77.5%, v11: 79.2%). Compared baseline YOLOv8 model, achieves 4.2% improvement mAP, which statistically (P < 0.01). These findings indicate offers valid solution precise

Language: Английский

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Transformative Role of Artificial Intelligence in Advancing Sustainable Tomato (Solanum lycopersicum) Disease Management for Global Food Security: A Comprehensive Review

Sustainability, Journal Year: 2023, Volume and Issue: 15(15), P. 11681 - 11681

Published: July 28, 2023

The growing global population and accompanying increase in food demand has put pressure on agriculture to produce higher yields the face of numerous challenges, including plant diseases. Tomato is a widely cultivated essential crop that particularly susceptible disease, resulting significant economic losses hindrances security. Recently, Artificial Intelligence (AI) emerged as promising tool for detecting classifying tomato leaf diseases with exceptional accuracy efficiency, empowering farmers take proactive measures prevent damage production loss. AI algorithms are capable processing vast amounts data objectively without human bias, making them potent even subtle variations traditional techniques might miss. This paper provides comprehensive overview most recent advancements disease classification using Machine Learning (ML) Deep (DL) techniques, an emphasis how these approaches can enhance effectiveness classification. Several ML DL models, convolutional neural networks (CNN), evaluated review highlights various features used acquisition well evaluation metrics employed assess performance models. Moreover, this emphasizes address limitations classification, leading improved more efficient management ultimately contributing concludes by outlining research proposing new directions field AI-assisted These insights will be value researchers professionals interested utilizing contribute sustainable (SDG-3).

Language: Английский

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A ResNet50-DPA model for tomato leaf disease identification

Frontiers in Plant Science, Journal Year: 2023, Volume and Issue: 14

Published: Oct. 16, 2023

Tomato leaf disease identification is difficult owing to the variety of diseases and complex causes, for which method based on convolutional neural network effective. While it challenging capture key features or tends lose a large number when extracting image by applying this method, resulting in low accuracy identification. Therefore, ResNet50-DPA model proposed identify tomato paper. Firstly, an improved ResNet50 included model, replaces first layer convolution basic with cascaded atrous convolution, facilitating obtaining different scales. Secondly, dual-path attention (DPA) mechanism search features, where stochastic pooling employed eliminate influence non-maximum values, two convolutions one dimension are introduced replace MLP effectively reducing damage information. In addition, quickly accurately type disease, DPA module incorporated into residual obtain enhanced feature map, helps reduce economic losses. Finally, visualization results Grad-CAM presented show that can more improve interpretability meeting need precise diseases.

Language: Английский

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A Transfer Learning-Based Framework: MobileNet-SVM for Efficient Tomato Leaf Disease Classification

Published: May 2, 2024

In the face of a burgeoning global population exceeding seven billion and dwindling agricultural land, plants remain pivotal for sustaining human civilization's food needs. However, plant health is threatened by various diseases, particularly leaf ailments like spots, bacterial infections, black spots. These afflictions, predominantly caused bacteria fungi, jeopardize crop yields. Timely disease detection imperative safeguarding productivity. This study introduces novel hybrid approach amalgamating MobileNet, transfer learning-based model, with SVM (Support Vector Machine) hinge loss. Leveraging MobileNet's pre-trained capabilities, features are extracted fed into an classifier to discern nine distinct types tomato diseases healthy leaves. Statistical analysis underscores efficacy this surpassing previous benchmarks. Notably, it achieves exceptional classification accuracy, precision, recall, AUC values, culminating in impressive overall accuracy 99.37%.

Language: Английский

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MiniTomatoNet: a lightweight CNN for tomato leaf disease recognition on heterogeneous FPGA-SoC

The Journal of Supercomputing, Journal Year: 2024, Volume and Issue: 80(15), P. 21837 - 21866

Published: June 17, 2024

Language: Английский

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